Cyberbullying is a pervasive issue on online platforms, yet early intervention via predictive modeling remains an open challenge. This challenge is compounded by the temporal dynamics of user interactions and the sparsity of such interactions in real-world social networks, making reliable modeling difficult. Current methods predominantly focus on detecting cyberbullying after it occurs through user content and profiles, while overlooking the temporal patterns and struggling when social interaction data is limited. We propose TemSoGraph, a unified temporal social graph learning model for cyberbullying detection and prediction. The model leverages a temporal self-attention mechanism to capture time-evolving user interactions and employs joint global and local node updates to represent users with limited interactions. It further incorporates a domain adaptor that learns domain-invariant features, enhancing generalization across datasets even when labeled target data is scarce. Experiments on two real-world datasets, Instagram and Vine, show that TemSoGraph outperforms eight cyberbullying detection models in detection task and six dynamic graph neural networks in prediction task. On the prediction task, TemSoGraph achieves a recall of 97.18% on Instagram with 2.53% improvement and 93.38% on Vine with 6.25% improvement. The model supports both detection and future prediction and provides a strong benchmark for cyberbullying modeling. • We propose TemSoGraph model for cyberbullying detection and prediction. • TemSoGraph works effectively under real-world data sparsity problem. • TemSoGraph integrates domain adaptor for cross-dataset generalization.

EEG-based biometric systems verify the identity of a user by comparing the probe to a reference EEG template of the claimed user enrolled in the system, or by classifying the probe against a user verification model stored in the system. These approaches are often referred to as template-based and model-based methods, respectively. Compared with template-based methods, model-based methods, especially those based on deep learning models, tend to provide enhanced performance and more flexible applications. However, there is no public research report on the security and cancellability issue for model-based approaches. This becomes a critical issue considering the growing popularity of deep learning in EEG biometric applications. In this study, we investigate the security issue of deep learning model-based EEG biometric systems, and demonstrate that model inversion attacks post a threat for such model-based systems. That is to say, an adversary can produce synthetic data based on the output and parameters of the user verification model to gain unauthorized access by the system. We propose a cancellable deep learning framework to defend against such attacks and protect system security. The framework utilizes a generative adversarial network to approximate a non-invertible transformation whose parameters can be changed to produce different data distributions. A user verification model is then trained using output generated from the generator model, while information about the transformation is discarded. The proposed framework is able to revoke compromised models to defend against hill climbing attacks and model inversion attacks. Evaluation results show that the proposed method, while being cancellable, achieves better verification performance than the template-based methods and state-of-the-art non-cancellable deep learning methods.

Wearable devices-based biometrics has become mainstream in the biometric domain, especially in mobile computing, due to its convenience, flexibility, and potentially high user acceptance. Among various modalities, wearable devices-based gait recognition has been recognized as an effective user authentication method and employed in various applications, such as automated entry systems for home, school, work, vehicles, and automated ticket payment/validation for public transport. However, how secure wearable gait remains an open research question. In this study, we conduct a comprehensive security analysis of the wearable gait. Then, we demonstrate that gait itself is not robust against some attacking methods, such as spoofing or forgery. Therefore, we argue that an anti-spoofing mechanism is important for enhancing the security of wearable gait biometric systems. To this end, we proposed a novel authentication protocol called$Pistis$that embedded gait biometrics and a liveness detection mechanism that is aiming to detect various attacks of gait authentication systems. Our extensive experiments based on 50 subjects demonstrate that$Pistis$is effective in liveness detection and authentication performance enhancement, providing 100% accuracy for human and nonhuman detection, and 99.53% accuracy for user authentication. Pistis can be used as a liveness detection method for wearable devices-based biometrics, significantly for wearable gait.

Recent findings confirm that biometric templates derived from electroencephalography (EEG) signals contain sensitive information about registered users, such as age, gender, cognitive ability, mental status and health information. Existing privacy-preserving methods such as hash function and fuzzy commitment are not cancelable, where raw biometric features are vulnerable to hill-climbing attacks. To address this issue, we propose the PolyCosGraph, a system based onPolynomial transformation embeddingCosine functions withGraphfeatures of EEG signals, which is a privacy-preserving and cancelable template design that protects EEG features and system security against multiple attacks. In addition, a template corrupting process is designed to further enhance the security of the system, and a corresponding matching algorithm is developed. Even when the transformed template is compromised, attackers cannot retrieve raw EEG features and the compromised template can be revoked. The proposed system achieves the authentication performance of 1.49% EER with a resting state protocol, 0.68% EER with a motor imagery task, and 0.46% EER under a watching movie condition, which is equivalent to that in the non-encrypted domain. Security analysis demonstrates that our system is resistant to attacks via record multiplicity, preimage attacks, hill-climbing attacks, second attacks and brute force attacks.

As the human hand makes direct physical contact with smartphones, significant efforts have recently been made to study the behavioral information of hand gripping of smartphones for user authentication purposes. Most existing methods leverage hand gripping behavior (e.g., gripping gesture, gripping position, gripping strength) of smartphones as biometrics to identify users. However, behavioral-based biometric authentication approaches may suffer from two problems: authentication performance (accuracy) degradation due to high-intra class variations arising from changes in user behavior over time, and vulnerability under spoofing attacks. To address these issues, we propose HoldPass, which is a behavior-independent in-hand user authentication method using vibration. HoldPass is able to adapt to the changes of hand gripping behavior of smartphones by extracting unique and stable physical features of human hands and eliminating the behavior-related prior information. Specifically, in HoldPass, we propose an adversarial neural network to achieve authentication based on unique physical features. Experiments with 10 users show that HoldPass can authenticate users with 97.39% accuracy while keeping False Accepted Rates (FAR) at a minimum of 2.1%.

A single dataset could hide a significant number of relationships among its feature set. Learning these relationships simultaneously avoids the time complexity associated with running the learning algorithm for every possible relationship, and affords the learner with an ability to recover missing data and substitute erroneous ones by using available data. In our previous research, we introduced the gate-layer autoencoders (GLAEs), which offer an architecture that enables a single model to approximate multiple relationships simultaneously. GLAE controls what an autoencoder learns in a time series by switching on and off certain input gates, thus, allowing and disallowing the data to flow through the network to increase network's robustness. However, GLAE is limited to binary gates. In this article, we generalize the architecture to weighted gate layer autoencoders (WGLAE) through the addition of a weight layer to update the error according to which variables are more critical and to encourage the network to learn these variables. This new weight layer can also be used as an output gate and uses additional control parameters to afford the network with abilities to represent different models that can learn through gating the inputs. We compare the architecture against similar architectures in the literature and demonstrate that the proposed architecture produces more robust autoencoders with the ability to reconstruct both incomplete synthetic and real data with high accuracy.

As a promising candidate to complement traditional biometric modalities, brain biometrics using electroencephalography (EEG) data has received a widespread attention in recent years. However, compared with existing biometrics such as fingerprints and face recognition, research on EEG biometrics is still in its infant stage. Most of the studies focus on either designing signal elicitation protocols from the perspective of neuroscience or developing feature extraction and classification algorithms from the viewpoint of machine learning. These studies have laid the ground for the feasibility of using EEG as a biometric verification modality, but they have also raised security and privacy concerns as EEG data contains sensitive information. Existing research has used hash functions and cryptographic schemes to protect EEG data, but they do not provide functions for revoking compromised templates as in cancellable template design. This paper proposes the first cancellable EEG template design for privacy-preserving EEG-based verification systems, which can protect raw EEG signals containing sensitive privacy information (e.g., identity, health and cognitive status). A novel cancellable EEG template is developed based on EEG features extracted by a deep learning model and a non-invertible transform. The proposed transformation provides cancellable templates, while taking advantage of EEG elicitation protocol fusion to enhance biometric performance. The proposed verification system offers superior performance than the state-of-the-art, while protecting raw EEG data. Furthermore, we analyze the system’s capacity for resisting multiple attacks, and discuss some overlooked but critical issues and possible pitfalls involving hill-climbing attacks, second attacks, and classification-based verification systems.

As the volume of scientific papers grows rapidly in size, knowledge management for scientific publications is greatly needed. Information extraction and knowledge fusion techniques have been proposed to obtain information from scholarly publications and build knowledge repositories. However, retrieving the knowledge of problem/solution from academic papers to support users on solving specific research problems is rarely seen in the state of the art. Therefore, to remedy this gap, a knowledge-driven solution support system (K3S) is proposed in this paper to extract the information of research problems and proposed solutions from academic papers, and integrate them into knowledge maps. With the bibliometric information of the papers, K3S is capable of providing recommended solutions for any extracted problems. The subject of intrusion detection is chosen for demonstration in which required information is extracted with high accuracy, a knowledge map is constructed properly, and solutions to address intrusion problems are recommended.

Highly secure access control requires Swiss-cheese-type multi-layer security protocols. The use of electroencephalogram (EEG) to provide cognitive indicators for human workload and fatigue has created environments where the EEG data are well-integrated into systems, making it readily available for more forms of innovative uses including biometrics. However, most of the existing studies on EEG biometrics rely on resting state signals or require specific and repetitive sensory stimulation, limiting their uses in naturalistic settings. Moreover, the limited discriminatory power of uni-variate measures denies an opportunity to use dependences information inherent in brain regions to design more robust biometric identifiers. In this paper, we proposed a novel model for ongoing EEG biometric identification using EEG collected during a diverse set of tasks. The novelty lies in representing EEG signals as a graph based on within-frequency and cross-frequency functional connectivity estimates, and the use of graph convolutional neural network (GCNN) to automatically capture deep intrinsic structural representations from the EEG graphs for person identification. An extensive investigation was carried out to assess the robustness of the method against diverse human states, including resting states under eye-open and eye-closed conditions and active states drawn during the performance of four different tasks. We compared our method with the state-of-the-art EEG features, classifiers, and models of EEG biometrics. Results show that the representation drawn from EEG functional connectivity graphs demonstrates more robust biometric traits than direct use of uni-variate features. Moreover, the GCNN can effectively and efficiently capture discriminative traits, thus generalizing better over diverse human states.

Entropy has been widely investigated as an effective metric to evaluate the dynamic complexity of signals. EEG is biological signals that contain rich complex dynamics. Transforming the information encoded in the rich dynamics embedded within EEG into appropriate biomarkers with discriminatory powers is important for event detection. It has broad prospects in a wide range of applications including medical diagnosis, therapy, and rehabilitation. This paper proposes a new entropy-based measure, Multi-scale Weighted Inherent Fuzzy Entropy (WIFEn), as an effective EEG biomarker for improving event detection performance. WIFEn first extracts Inherent Mode Functions (IMFs) using the Empirical Mode Decomposition method, then uses a weighted sum scheme to fuse the fuzzy entropy metrics calculated on each IMF. Finally, the multi-scale variation accounts for the multi-timescale dynamics inherent in EEG signals. Since EEG signals are a superposition of series of oscillations where information embedded in these oscillations is useful for estimating signal complexity, the aforementioned decomposition, and weighted sum procedures can improve the estimation results. The proposed method is tested with three entropy-based metrics for two tasks. The first task is eye-open and eye-closed detection with resting state EEG signals recorded from 10 subjects; while the second task is seizure detection for 8 epilepsy patients. The results indicate that the multi-scale WIFEn provides a better discriminatory power that improves detection performance than classic entropy-based measures, with an averaged improvement of 13.7% (p-value <; 0.05) for resting-state classification and 5.9% (p-value <; 0.05) for seizure detection.

Electroencephalography (EEG) is one of the most promising methods in the field of Brain-Computer Interfaces (BCIs) due to its rich time-domain resolution and the availability of advanced and portable sensor technology. One of the major challenges for EEG signal analysis is the small size of its datasets as it is usually demanding for human subjects to perform lengthy experiments. Consequently, this challenge can limit the performance of EEG signal classification models. In this paper, we propose a novel generative adversarial network (GAN) model that can learn the statistical characteristics of the EEG signal and augment its datasets size to enhance the performance of classification models. Results show that the proposed model significantly outperforms other generative models on the utilized EEG dataset. Furthermore, it significantly enhances the performance of classification models working on small size EEG datasets after augmenting them with generated samples.

EEG signals could reveal unique information of an individual's brain activities. They have been regarded as one of the most promising biometric signals for person identification and verification. Steady-State Visual Evoked Potentials (SSVEPs), as EEG responses to visual stimulations at specific frequencies, could provide biometric information. However, current methods on SSVEP biometrics with hand-crafted power spectrum features and canonical correlation analysis (CCA) present only a limited range of individual distinctions and suffer relatively low accuracy. In this paper, we propose convolution neural networks (CNNs) with raw SSVEPs for person identification and verification without the need for any hand-crafted features. We conduct a comprehensive comparison between the performance of CNN with raw signals and a number of classical methods on two SSVEP datasets consisting of four and ten subjects, respectively. The proposed method achieved an averaged identification accuracy of 96.8%±0.01, which outperformed the other methods by an average of 45.5% (p-value <; 0.05). In addition, it achieved an averaged False Acceptance Rate (FAR) of 1.53%±0.01 and True Acceptance Rate (TAR) of 97.09%±0.02 for person verification. The averaged verification accuracy is 98.34% ± 0.01, which outperformed the other methods by an average of 11.8% (p-value <; 0.05). The proposed method based on deep learning offers opportunities to design a general-purpose EEG-based biometric system without the need for complex pre-processing and feature extraction techniques, making it feasible for real-time embedded systems.

Human identity is a prerequisite for trust assurance and assessment, which is essential for effective human-machine interaction in trusted autonomous systems. Unlike conventional authentication methods which do not require users to re-authenticate themselves for sustained access, continuous authentication affirms human identity in real-time, therefore is a solution for continued access monitoring in trusted autonomous systems. Robust continuous authentication needs robust multi-modal data sources. In this paper, we design a multi-modal biometrics system that continuously verifies the presence of a logged-in user. Two types of biometric data are used, face images and Electroencephalography (EEG) signals. Information from individual modalities is fused at matching score level. For face modality, matching scores are calculated by distances between eigenface coefficients. While for EEG signals, an event-related potential (ERP) modality is established by a simple ERP elicitation protocol and calculation of cross-correlation similarities. Scores from the two modalities are normalized and fused using three schemes, namely the sum-score, max-score and min-score scheme. The experiments reveal that individual variations found in the ERPs are detectable and can be used for continuous authentication. This is an interesting finding which indicates that the ERP biometrics are feasible for user authentication and worthy of further research. Results also show that combining ERP biometric with face biometric using sum-score scheme outperforms each modality in isolation. This piece of finding indicates the potential of integrating ERP into multimodal authentication systems.